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Jiang Wang

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16 papers

2 author rows

EAAI Journal 2026 Journal Article

Asynchronous multithreading reinforcement learning with attention-based significance measurement for collision-free robot navigation

Chao Sun
Jiang Wang
Xing Wu
Chaoxu Mu
Changyin Sun

Collision avoidance is one crucial technique to achieve safe and efficient robotic vehicle navigation in unknown environments. However, moving obstacles with unpredictability in dynamic scenarios, usually increase the difficulty and complexity in collision avoidance of robotic vehicles. To enhance the stability of collision avoidance and boost its adaptability to uncertain dynamic scenes, a new attention-based significance measurement actor–critic (ASMAC) architecture is proposed. It is an end-to-end robot navigation model that uses imperfect local observation to directly plan precise collision-free motion commands. Firstly, a significance-measured rollout replaybuffer (SMRR) is presented to categorize the experiences into different pools. It can prevent any overfitting or bias that may result from repeatedly sampling experience of a certain type during policy learning. Then, we enhance the traditional actor–critic network by integrating a multi-head local attention module to extract the local information at entity level. This way, the collision avoidance system can focus on key environmental features to compute more lightweight and respond more swiftly to dynamic changes in environment. Besides, a multi-step lookahead prediction (MLP) reward function is designed in the ASMAC-based reinforcement learning (RL) framework to prevent the generation of unnatural, intrusive, and short-sighted motion decisions. Finally, the asynchronous multithreading (AM) mechanism and proximal policy optimization (PPO) algorithm are extended to ASMAC model to offload the expensive online computation to an offline training process, enhancing the exploration efficiency in navigation policy learning of robotic vehicles. Extensive simulation and real-world physical experiments show that our method can generate time-efficient and collision-free guide paths in complex dynamic scenes, to successfully dodge collisions while moving towards the goal.

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JBHI Journal 2026 Journal Article

Spatiospectral Representation and Neural Decoding of Somatic Perception of Acupuncture Stimulations

Haitao Yu
Zaidong Lin
Fan Li
Jialin Liu
Chen Liu
Jiang Wang

Characterizing the neural representations underlying somatic perception is crucial for neural decoding of external stimulations. Acupuncture is an important therapeutic method of traditional Chinese medicine and can effectively modulate brain activity for the treatment of neural diseases. In this work, we investigate the neural representations based on the power spectral density (PSD) estimated from electroencephalogram (EEG) across the whole brain with deep learning. Frequency and spatial characteristics of PSD can reliably represent the dynamical brain responses to acupuncture with different manipulations, manifesting enhanced alpha power in parietal lobe. By removing aperiodic components, periodic spatial spectrum shows a higher representation ability of different brain states during acupuncture stimulations, and twiring-rotating (TR) manipulation have a more pronounced modulatory effect than lifting-thrusting (LT) manipulation. Moreover, we further infer the low-dimensional feature-disentangled representations with generative adversarial network (GAN), i. e. , w -latents of StyleGAN, which can capture the latent features of periodic spatial spectrum and strike a balance between separability and generalizability. The effectiveness of feature-disentangled representations is evaluated by decoding the acupuncture states, which can achieve a highest accuracy of 95. 71% with Transformer classifier. Compared with high-dimensional spatial spectrum, low-dimensional latent features can best characterize different brain states, indicating a precise representation of somatic perception of acupuncture stimulations. Taken together, our results highlight the significant role of spatial spectral representation underlying somatic perception and serve as an important benchmark for the evaluation of acupuncture effect on human brain.

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AAAI Conference 2026 Conference Paper

Unsupervised Single-Channel Audio Separation with Diffusion Source Priors

Runwu Shi
Chang Li
Jiang Wang
Rui Zhang
Nabeela Khan
Benjamin Yen
Takeshi Ashizawa
Kazuhiro Nakadai

Single-channel audio separation aims to separate individual sources from a single-channel mixture. Most existing methods rely on supervised learning with synthetically generated paired data. However, obtaining high-quality paired data in real-world scenarios is often difficult. This data scarcity can degrade model performance under unseen conditions and limit generalization ability. To this end, in this work, we approach this problem from an unsupervised perspective, framing it as a probabilistic inverse problem. Our method requires only diffusion priors trained on individual sources. Separation is then achieved by iteratively guiding an initial state toward the solution through reconstruction guidance. Importantly, we introduce an advanced inverse problem solver specifically designed for separation, which mitigates gradient conflicts caused by interference between the diffusion prior and reconstruction guidance during inverse denoising. This design ensures high-quality and balanced separation performance across individual sources. Additionally, we find that initializing the denoising process with an augmented mixture instead of pure Gaussian noise provides an informative starting point that significantly improves the final performance. To further enhance audio prior modeling, we design a novel time–frequency attention-based network architecture that demonstrates strong audio modeling capability. Collectively, these improvements lead to significant performance gains, as validated across speech–sound event, sound event, and speech separation tasks.

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EAAI Journal 2025 Journal Article

A multi-scale feature fusion network based on semi-channel attention for seismic phase picking

Shuguang Zhao
Jiang Wang
Ping Huang
Fa Zhao
Fudong Zhang
Yadongyang Zhu

In the field of seismic data processing, deep learning technologies have been widely used for seismic phase picking. However, it is difficult to take full advantage of the features extracted at different stages in existing models. In this paper, a multi-scale feature fusion network was proposed for seismic phase picking to address this problem. In the stage of feature extraction, semi-channel attention is introduced. It improves the representation ability of the model by efficiently utilizing the feature information extracted from the encoder. In the stage of decoding, a channel compression module is designed to reduce the number of feature channels. It improves the receptive field of channels. Additionally, a multi-feature fusion module is presented to integrate features at multiple scales. It reduces the loss of useful information and improves the accuracy of phase picking. The effectiveness of our network is validated on Stanford earthquake dataset, where the picking errors for phase picking are 2 ms. The parameter of our network is only 52, 100. Compared with earthquake transformer, it has 42. 1% fewer time costs to process 12, 656 test samples on Graphics Processing Unit.

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JBHI Journal 2025 Journal Article

Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface

Haitao Yu
Fanyi Zeng
Dongliang Liu
Jiang Wang
Jialin Liu

Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain responses is still unclear. We proposed a deep learning framework using electroencephalographic activity of stimulated subjects to decode the needling processes of various acupuncture manipulations performed on Zusanli acupoint. Contrastive representation learning integrated with domain adaptation strategy was applied to estimate 3D hand postures and hand joint motion trajectories of acupuncturist with video recordings, by which finite dimensional representations of behavior manifolds for needling operations were inferred. Distinct transition dynamics of behavior manifold were observed for acupuncture with lifting-thrusting and twisting-rotating manipulations. Moreover, latent neural manifolds of acupuncture evoked EEG signals were estimated in low dimensional state space of brain activities with unsupervised manifold learning, which can reliably represent acupuncture stimulations. Furthermore, a nonlinear decoder based on neural networks was designed to transform neural manifolds to behavior manifolds and further predict acupuncture manipulation as well as needling process. Experimental results demonstrated a high performance of the proposed decoding framework for four types of acupuncture manipulations with a precision of 92. 42%. The EEG decoder provides an acupuncture-brain interface linking somatosensory stimulations with neural representations, an effective scheme for revealing clinical efficacy of acupuncture treatment.

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IROS Conference 2025 Conference Paper

Single-Microphone-Based Sound Source Localization for Mobile Robots in Reverberant Environments

Jiang Wang
Runwu Shi
Benjamin Yen 0001
He Kong 0001
Kazuhiro Nakadai

Accurately estimating sound source positions is crucial for robot audition. However, existing sound source localization methods typically rely on a microphone array with at least two spatially preconfigured microphones. This requirement hinders the applicability of microphone-based robot audition systems and technologies. To alleviate these challenges, we propose an online sound source localization method that uses a single microphone mounted on a mobile robot in reverberant environments. Specifically, we develop a lightweight neural network model with only 43k parameters to perform real-time distance estimation by extracting temporal information from reverberant signals. The estimated distances are then processed using an extended Kalman filter to achieve online sound source localization. To the best of our knowledge, this is the first work to achieve online sound source localization using a single microphone on a moving robot, a gap that we aim to fill in this work. Extensive experiments demonstrate the effectiveness and merits of our approach. To benefit the broader research community, we have open-sourced our code at https://github.com/JiangWAV/single-mic-SSL.

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IROS Conference 2024 Conference Paper

Asynchronous Microphone Array Calibration using Hybrid TDOA Information

Chengjie Zhang
Jiang Wang
He Kong

Asynchronous microphone array calibration is a prerequisite for many audition robot applications. A popular solution to the above calibration problem is the batch form of Simultaneous Localisation and Mapping (SLAM), using the time difference of arrival measurements between two microphones (TDOA-M), and the robot (which serves as a moving sound source during calibration) odometry information. In this paper, we introduce a new form of measurement for microphone array calibration, i. e. the time difference of arrival between adjacent sound events (TDOA-S) with respect to the microphone channels. We propose to use TDOA-S and TDOA-M, called hybrid TDOA, together with odometry measurements for bath SLAM-based calibration of asynchronous microphone arrays. Extensive simulation and real-world experiments show that our method is more independent of microphone number, less sensitive to initial values (when using off-the-shelf algorithms such as Gauss-Newton iterations), and has better calibration accuracy and robustness under various TDOA noises. Simulation results also demonstrate that our method has a lower Cramér-Rao lower bound (CRLB) for microphone parameters. To benefit the community, we open-source our code and data at https://github.com/AISLAB-sustech/Hybrid-TDOA-Calib.

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IROS Conference 2024 Conference Paper

I-ASM: Iterative Acoustic Scene Mapping for Enhanced Robot Auditory Perception in Complex Indoor Environments

Linya Fu
Yuanzheng He
Jiang Wang
Xu Qiao
He Kong

This paper addresses the challenge of acoustic scene mapping (ASM) in complex indoor environments with multiple sound sources. Unlike existing methods that rely on prior data association or SLAM frameworks, we propose a novel particle filter-based iterative framework, termed I-ASM, for ASM using a mobile robot equipped with a microphone array and LiDAR. I-ASM harnesses an innovative "implicit association" to align sound sources with Direction of Arrival (DoA) observations without requiring explicit pairing, thereby streamlining the mapping process. Given inputs including an occupancy map, DoA estimates from various robot positions, and corresponding robot pose data, I-ASM performs multi-source mapping through an iterative cycle of "Filtering-Clustering-Implicit Associating". The proposed framework has been tested in real-world scenarios with up to 10 concurrent sound sources, demonstrating its robustness against missing and false DoA estimates while achieving high-quality ASM results. To benefit the community, we open-source all the codes and data at https://github.com/AISLAB-sustech/Acoustic-Scene-Mapping

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JBHI Journal 2023 Journal Article

An Enhanced EEG Microstate Recognition Framework Based on Deep Neural Networks: An Application to Parkinson's Disease

Chunguang Chu
Zhen Zhang
Zhenxi Song
Zifan Xu
Jiang Wang
Fei Wang
Wei Liu
Liying Lu

Variations in brain activity patterns reveal impairments of motor and cognitive functions in the human brain. Electroencephalogram (EEG) microstates embody brain activity patterns at a microscopic time scale. However, current microstate analysis method can only recognize less than 90% of EEG signals per subject, which severely limits the characterization of dynamic brain activity. As an application to early Parkinson's disease (PD), we propose an enhanced EEG microstate recognition framework based on deep neural networks, which yields recognition rates from 90% to 99%, as accompanied by a strong anti-artifact property. Additionally, gradient-weighted class activation mapping, as a visualization technique, is employed to locate the activated functional brain regions of each microstate class. We find that each microstate class corresponds to a particular activated brain region. Finally, based on the improved identification of microstate sequences, we explore the EEG microstate characteristics and their clinical associations. We show that the decreased occurrences of a particular microstate class reflect the degree of cognitive decline in early PD, and reduced transitions between certain microstates suggest injury in motor-related brain regions. The novel EEG microstate recognition framework paves the way to revealing more effective biomarkers for early PD.

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EAAI Journal 2022 Journal Article

Application of multi-objective particle swarm optimization based on short-term memory and K-means clustering in multi-modal multi-objective optimization

Yang Yang
Qianfeng Liao
Jiang Wang
Yuan Wang

To solve the multi-modal multi-objective optimization problems in which the same Pareto Front (PF) may correspond to multiple different Pareto Optimal Sets (PSs), an improved multi-objective particle swarm optimizer with short-term memory and K-means clustering (MOPSO-SMK) is proposed in this paper. According to the framework of multi-objective particle swarm optimization (MOPSO) algorithm, the designs of updating mechanism and population maintenance mechanism are the keys to obtain the optimal solutions. As a significant influence factor of the updating mechanism, the inertia weight has been discussed in this paper. In the improved algorithm, a new update model for the value of pbest based on short-term memory is proposed. The update strategies based on K-means clustering are adopted to obtain the better gbest and elite archive. 16 multi-modal multi-objective optimization functions are used to verify the feasibility and effectiveness of the proposed MOPSO-SMK. As the results show, MOPSO-SMK has more advantages in four indexes (1/PSP, 1/HV, IGDX, and IGDF) compared with other three multi-objective optimization algorithms.

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IJCAI Conference 2022 Conference Paper

Augmenting Knowledge Graphs for Better Link Prediction

Jiang Wang
Filip Ilievski
Pedro Szekely
Ke-Thia Yao

Embedding methods have demonstrated robust performance on the task of link prediction in knowledge graphs, by mostly encoding entity relationships. Recent methods propose to enhance the loss function with a literal-aware term. In this paper, we propose KGA: a knowledge graph augmentation method that incorporates literals in an embedding model without modifying its loss function. KGA discretizes quantity and year values into bins, and chains these bins both horizontally, modeling neighboring values, and vertically, modeling multiple levels of granularity. KGA is scalable and can be used as a pre-processing step for any existing knowledge graph embedding model. Experiments on legacy benchmarks and a new large benchmark, DWD, show that augmenting the knowledge graph with quantities and years is beneficial for predicting both entities and numbers, as KGA outperforms the vanilla models and other relevant baselines. Our ablation studies confirm that both quantities and years contribute to KGA's performance, and that its performance depends on the discretization and binning settings. We make the code, models, and the DWD benchmark publicly available to facilitate reproducibility and future research.

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JBHI Journal 2022 Journal Article

Gating Attractor Dynamics of Frontal Cortex Under Acupuncture via Recurrent Neural Network

Kai Li
Jiang Wang
Zhicai Hu
Bin Deng
Haitao Yu

Acupuncture can regulate the functions of human body and improve the cognition of brain. However, the mechanism of acupuncture manipulations remains unclear. Here, we hypothesis that the frontal cortex plays a gating role in information routing of brain network under acupuncture. To that end, the gating effect of frontal cortex under acupuncture is analyzed in combination with EEG data of acupuncture at Zusanli acupoints. In addition, recurrent neural network (RNN) is used to reproduce the dynamics of frontal cortex under normal state and acupuncture state. From low-dimensional view, it is shown that the brain networks under acupuncture state can show stable attractor cycle dynamics, which may explain the regulation effect of acupuncture. Comparing with different manipulations, we find that the attractor of low-dimensional trajectory varies under different frequencies of acupuncture. Besides, a strip gated band of neural dynamics is found by changing the frequency of stimulation and excitatory-inhibitory balance of network. This reverse engineering of brain network indicates that the differences among acupuncture manipulations are caused by interaction and separation in the neural activity space between attractors that encode acupuncture function. Consequently, our results may provide help for quantitative analysis of acupuncture, and benefit for the clinical guidance of acupuncture clinicians.

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YNIMG Journal 2022 Journal Article

Subthalamic and pallidal stimulation in Parkinson's disease induce distinct brain topological reconstruction

Chunguang Chu
Naying He
Kristina Zeljic
Zhen Zhang
Jiang Wang
Jun Li
Yu Liu
Youmin Zhang

The subthalamic nucleus (STN) and globus pallidus internus (GPi) are the two most common and effective target brain areas for deep brain stimulation (DBS) treatment of advanced Parkinson's disease. Although DBS has been shown to restore functional neural circuits of this disorder, the changes in topological organization associated with active DBS of each target remain unknown. To investigate this, we acquired resting-state functional magnetic resonance imaging (fMRI) data from 34 medication-free patients with Parkinson's disease that had DBS electrodes implanted in either the subthalamic nucleus or internal globus pallidus (n = 17 each), in both ON and OFF DBS states. Sixteen age-matched healthy individuals were used as a control group. We evaluated the regional information processing capacity and transmission efficiency of brain networks with and without stimulation, and recorded how stimulation restructured the brain network topology of patients with Parkinson's disease. For both targets, the variation of local efficiency in motor brain regions was significantly correlated (p < 0.05) with improvement rate of the Uniform Parkinson's Disease Rating Scale-III scores, with comparable improvements in motor function for the two targets. However, non-motor brain regions showed changes in topological organization during active stimulation that were target-specific. Namely, targeting the STN decreased the information transmission of association, limbic and paralimbic regions, including the inferior frontal gyrus angle, insula, temporal pole, superior occipital gyri, and posterior cingulate, as evidenced by the simultaneous decrease of clustering coefficient and local efficiency. GPi-DBS had a similar effect on the caudate and lenticular nuclei, but enhanced information transmission in the cingulate gyrus. These effects were not present in the DBS-OFF state for GPi-DBS, but persisted for STN-DBS. Our results demonstrate that DBS to the STN and GPi induce distinct brain network topology reconstruction patterns, providing innovative theoretical evidence for deciphering the mechanism through which DBS affects disparate targets in the human brain.

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YNICL Journal 2020 Journal Article

Spatiotemporal EEG microstate analysis in drug-free patients with Parkinson's disease

Chunguang Chu
Xing Wang
Lihui Cai
Lei Zhang
Jiang Wang
Chen Liu
Xiaodong Zhu

The clinical diagnosis of Parkinson's disease (PD) is very difficult, especially in the early stage of the disease, because there is no physiological indicator that can be referenced. Drug-free patients with early PD are characterized by clinical symptoms such as impaired motor function and cognitive decline, which was caused by the dysfunction of brain's dynamic activities. The indicators of brain dysfunction in patients with PD at an early unmedicated condition may provide a valuable basis for the diagnosis of early PD and later treatment. In order to find the spatiotemporal characteristic markers of brain dysfunction in PD, the resting-state EEG microstate analysis is used to explore the transient state of the whole brain of 23 drug-free patients with PD on the sub-second timescale compared to 23 healthy controls. EEG microstates reflect a transiently stable brain topological structure with spatiotemporal characteristics, and the spatial characteristic microstate classes and temporal parameters provide insight into the brain's functional activities in PD patients. The further exploration was to explore the relation between temporal microstate parameters and significant clinical symptoms to determine whether these parameters could be used as a basis for clinically assisted diagnosis. Therefore, we used a general linear model (GLM) to explore the relevance of microstate parameters to clinical scales and multiple patient attributes, and the Wilcoxon rank sum test was used to quantify the linear relation between influencing factors and microstate parameters. Results of microstate analysis revealed that there was an unique spatial microstate different from healthy controls in PD, and several other typical microstates had significant differences compared with the normal control group, and these differences were reflected in the microstate parameters, such as longer durations and more occurrences of one class of microstates in PD compared with healthy controls. Furthermore, correlation analysis showed that there was a significant correlation between multiple microstate classes' parameters and significant clinical symptoms, including impaired motor function and cognitive decline. These results indicate that we have found multiple quantifiable feature tags that reflect brain dysfunction in the early stage of PD. Importantly, such temporal dynamics in microstates are correlated with clinical scales which represent the motor function and recognize level. The obtained results may deepen our understanding of the brain dysfunction caused by PD, and obtain some quantifiable signatures to provide an auxiliary reference for the early diagnosis of PD.

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AAAI Conference 2017 Conference Paper

Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition

Xiao Liu
Jiang Wang
Shilei Wen
Errui Ding
Yuanqing Lin

A key challenge in ﬁne-grained recognition is how to ﬁnd and represent discriminative local regions. Recent attention models are capable of learning discriminative region localizers only from category labels with reinforcement learning. However, not utilizing any explicit part information, they are not able to accurately ﬁnd multiple distinctive regions. In this work, we introduce an attribute-guided attention localization scheme where the local region localizers are learned under the guidance of part attribute descriptions. By designing a novel reward strategy, we are able to learn to locate regions that are spatially and semantically distinctive with reinforcement learning algorithm. The attribute labeling requirement of the scheme is more amenable than the accurate part location annotation required by traditional part-based ﬁne-grained recognition methods. Experimental results on the CUB-200- 2011 dataset (Wah et al. 2011) demonstrate the superiority of the proposed scheme on both ﬁne-grained recognition and attribute recognition.

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ICRA Conference 1992 Conference Paper

3D relative position and orientation estimation using Kalman filter for robot control

Jiang Wang
William J. Wilson

A vision-based position sensing system which provides three-dimensional relative position and orientation (pose) of an arbitrary moving object with respect to a camera for a real-time tracking control is studied. Kalman filtering was applied to vision measurements for the implicit solution of the photogrametric equations and to provide significant temporal filtering of the resulting motion parameters resulting in optimal pose estimation. Both computer simulation and real-time experimental results are presented to verify the effectiveness of the Kalman filter approach with large vision measurement noise. >

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